Seizure prediction using cross-correlation and classification

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients. Cross-correlation coefficients are extracted every 2 seconds using a 4-second window with 50% overlap from focus electrodes identified by the epileptologist. Features are further processed by a second-order Kalman filter and then input to three different classifiers which include AdaBoost, radial basis function kernel support vector machine (RBF-SVM) and artificial neural network (ANN). The algorithm is tested on the long-term intra-cranial EEG (iEEG) database collected at the UMN epilepsy clinic. This database includes EEG recordings from 2 patients sampled from varying number of electrodes sampled at 2kHz. It is shown that the proposed algorithm achieves a high sensitivity and a low false positive rate.

Original languageEnglish (US)
Title of host publicationConference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781467385763
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


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